Virtual object machine learning

ABSTRACT

A machine learning scheme can be trained on a set of labeled training images of a subject in different poses, with different textures, and with different background environments. The label or marker data of the subject may be stored as metadata to a 3D model of the subject or rendered images of the subject. The machine learning scheme may be implemented as a supervised learning scheme that can automatically identify the labeled data to create a classification model. The classification model can classify a depicted subject in many different environments and arrangements (e.g., poses).

CLAIM OF PRIORITY

This application is a continuation of U.S. patent application Ser. No.17/322,609, filed May 17, 2021, which is a continuation of U.S. patentapplication Ser. No. 16/777,098, filed Jan. 30, 2020, which is acontinuation of U.S. patent application Ser. No. 15/653,186, filed Jul.18, 2017, each of which are hereby incorporated by reference herein intheir entireties.

TECHNICAL FIELD

Embodiments of the present disclosure relate generally to machinelearning and, more particularly, but not by way of limitation, tovirtual object machine learning.

BACKGROUND

Machine learning schemes can be trained to classify objects usingtraining data. For example, a support vector machine (SVM) can betrained on images of railroad tie-plates. After the SVM is trained, itcan receive an image of a tie-plate and output a likelihood that theimaged tie-plate is of a given type.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

To easily identify the discussion of any particular element or act, themost significant digit or digits in a reference number refer to thefigure (“FIG.”) number in which that element or act is first introduced.

FIG. 1 is a block diagram showing an example messaging system forexchanging data (e.g., messages and associated content) over a network.

FIG. 2 is block diagram illustrating further details regarding amessaging system having an integrated virtual object machine learningsystem, according to example embodiments.

FIG. 3 is a schematic diagram illustrating data which may be stored in adatabase of a messaging server system, according to certain exampleembodiments.

FIG. 4 is a schematic diagram illustrating a structure of a message,according to some embodiments, generated by a messaging clientapplication for communication.

FIG. 5 is a schematic diagram illustrating an example access-limitingprocess, in terms of which access to content (e.g., an ephemeralmessage, and associated multimedia payload of data) or a contentcollection (e.g., an ephemeral message story) may be time-limited (e.g.,made ephemeral).

FIG. 6 shows a functional architecture for a virtual object machinelearning system, according to some example embodiments.

FIG. 7 shows a flow diagram of a method for implementing virtual objectmachine learning, according to some example embodiments.

FIG. 8 shows a flow diagram of a method for generating training data,according to some example embodiments.

FIG. 9 shows an example rendering environment for generating trainingimages, according to some example embodiments.

FIG. 10 shows example structure of a 3D hand, according to some exampleembodiments.

FIG. 11 shows an environment texture for use as an environmentbackground, according to some example embodiments.

FIG. 12 shows example gestures of a 3D hand model, according to someexample embodiments.

FIG. 13 shows example poses of a 3D Labrador retriever, according tosome example embodiments.

FIG. 14 shows a client device implementing the virtual object machinelearning system, according to some example embodiments.

FIG. 15 is a block diagram illustrating a representative softwarearchitecture, which may be used in conjunction with various hardwarearchitectures herein described.

FIG. 16 is a block diagram illustrating components of a machine,according to some example embodiments, able to read instructions from amachine-readable medium (e.g., a machine-readable storage medium) andperform any one or more of the methodologies discussed herein.

DETAILED DESCRIPTION

The description that follows includes systems, methods, techniques,instruction sequences, and computing machine program products thatembody illustrative embodiments of the disclosure. In the followingdescription, for the purposes of explanation, numerous specific detailsare set forth in order to provide an understanding of variousembodiments of the inventive subject matter. It will be evident,however, to those skilled in the art, that embodiments of the inventivesubject matter may be practiced without these specific details. Ingeneral, well-known instruction instances, protocols, structures, andtechniques are not necessarily shown in detail.

Machine learning schemes can be trained to classify objects usingtraining data. For example, a support vector machine (SVM) can betrained on images of railroad tie-plates. After the SVM is trained, itcan receive an image of a tie-plate and output a likelihood that theimaged tie-plate is of a given type.

While machine learning schemes have performed well in classifying staticor rigid items (e.g., tie-plates, hardware parts), it is far moredifficult for a machine learning scheme to classify highly dynamicsubjects. For example, using a machine learning scheme to classify a dogis difficult because a given dog can be of many different breeds, eachhaving different coloration and fur type. Further, classifying how agiven dog is posing (e.g., sitting, standing, tail up, tail down, earsup, ears down) is very complex as the coloration of dog fur, thefur-type, and the interplay between the environment lighting and itseffect on the dog create a myriad of different training samples which amachine learning scheme must learn. To learn, the machine learningscheme is trained on training data. However, generating training imagesof different breeds of dog, with different colorations, with differentfur-types, in different poses, in different lighting environments is notpractical; at least due to the difficulty of controlling the imagedsubjects (e.g., getting the subjects to the environments, posing thesubjects, etc.).

Further compounding the problem is pre-processing the training data toready it for machine learning. For some machine learning schemes (e.g.,supervised learning schemes), the training data is manually labeled toaid in generating the machine learning model. For example, to create aset of training data for hand gesture recognition, a human labeler maygo through each hand image in the training data and tag where the jointsare located, where the ends of the fingers are located, etc. However,accurately (e.g., manually) tagging very large sets of data is notpractical. Further, as mentioned above, the very large sets of data maynot be feasible to create in the first place since it requires a myriadof subjects (e.g., different types of dogs or cats, different variationsof humans) in different poses (e.g., gestures), in different lightingenvironments.

To this end, a virtual object machine learning system can use asynthetic set of training data to accurately classify highly dynamicsubjects. The synthetic set of training data can be created using a 3Dmodel of a subject, such as a human hand or a dog. The 3D model can beconstructed using a rigging structure (e.g., bones, skeleton), therebyenabling key points to more easily be tracked. For example, a 3D humanhand can be built around a skeleton, and the joints of the skeleton canbe stored as metadata to the model.

The 3D model can be readily (e.g., programmatically) arranged intodifferent poses, and in each pose, the key points will be tracked in themetadata. Further, textures (e.g., 3D model skins) can be readilyapplied to the 3D model to simulate different subject types (e.g.,different colorations of a dog, different breeds of dog, differentpigmentations of a human hand).

Further, the lighting environment can readily be controlled by applyingdifferent textures to a background surface, such as a sphere. Each point(e.g., pixel) in the background surface can be used to set lightingemanating from that point. Thus, different lighting environments (e.g.,indoors, outdoors, street lights, laboratory lighting) can be readilymanaged. In this way, a very large set of labeled training images can becreated and used to train a machine learning scheme. The trained machinelearning scheme can then accurately classify depicted objects.

FIG. 1 is a block diagram showing an example messaging system 100 forexchanging data (e.g., messages and associated content) over a network.The messaging system 100 includes multiple client devices 102, each ofwhich hosts a number of applications including a messaging clientapplication 104. Each messaging client application 104 iscommunicatively coupled to other instances of the messaging clientapplication 104 and a messaging server system 108 via a network 106(e.g., the Internet). In various embodiments, virtual machine learningcan be used by messaging client application 104 and/or image processingsystem 116 to analyze images sent within the messaging system, and touse this analysis to provide features within the messaging system.

Accordingly, each messaging client application 104 is able tocommunicate and exchange data with another messaging client application104 and with the messaging server system 108 via the network 106. Thedata exchanged between messaging client applications 104, and between amessaging client application 104 and the messaging server system 108,include functions (e.g., commands to invoke functions) as well aspayload data (e.g., text, audio, video, or other multimedia data).

The messaging server system 108 provides server-side functionality viathe network 106 to a particular messaging client application 104. Whilecertain functions of the messaging system 100 are described herein asbeing performed by either a messaging client application 104 or by themessaging server system 108, it will be appreciated that the location ofcertain functionality within either the messaging client application 104or the messaging server system 108 is a design choice. For example, itmay be technically preferable to initially deploy certain technology andfunctionality within the messaging server system 108, but to latermigrate this technology and functionality to the messaging clientapplication 104 where a client device 102 has a sufficient processingcapacity.

The messaging server system 108 supports various services and operationsthat are provided to the messaging client application 104. Suchoperations include transmitting data to, receiving data from, andprocessing data generated by the messaging client application 104. Thisdata may include message content, client device information, geolocationinformation, media annotation and overlays, message content persistenceconditions, image search, social network information, and live eventinformation, as examples, some of which rely on information generated byanalyzing images sent through the messaging system. Data exchangeswithin the messaging system 100 are invoked and controlled throughfunctions available via user interfaces (UIs) of the messaging clientapplication 104.

Turning now specifically to the messaging server system 108, anapplication programming interface (API) server 110 is coupled to, andprovides a programmatic interface to, an application server 112. Theapplication server 112 is communicatively coupled to a database server118, which facilitates access to a database 120 in which is stored dataassociated with messages processed by the application server 112. Insome embodiments, databases 120 may also store results of imageprocessing, or details of various trained and untrained support vectormachines that may be used by messaging server system 118.

The API server 110 receives and transmits message data (e.g., commandsand message payloads) between the client device 102 and the applicationserver 112. Specifically, the API server 110 provides a set ofinterfaces (e.g., routines and protocols) that can be called or queriedby the messaging client application 104 in order to invoke functionalityof the application server 112. The API server 110 exposes variousfunctions supported by the application server 112, including accountregistration; login functionality; the sending of messages, via theapplication server 112, from a particular messaging client application104 to another messaging client application 104; the sending of mediafiles (e.g., images or video) from a messaging client application 104 toa messaging server application 114 for possible access by anothermessaging client application 104; the setting of a collection of mediadata (e.g., a story); the retrieval of such collections; the retrievalof a list of friends of a user of a client device 102; the retrieval ofmessages and content; the addition and deletion of friends to and from asocial graph; the location of friends within the social graph; andapplication events (e.g., relating to the messaging client application104).

The application server 112 hosts a number of applications andsubsystems, including the messaging server application 114, an imageprocessing system 116, and a social network system 122. The messagingserver application 114 implements a number of message processingtechnologies and functions, particularly related to the aggregation andother processing of content (e.g., textual and multimedia content)included in messages received from multiple instances of the messagingclient application 104. As will be described in further detail, the textand media content from multiple sources may be aggregated intocollections of content (e.g., called stories or galleries). Thesecollections are then made available, by the messaging server application114, to the messaging client application 104. Other processor- andmemory-intensive processing of data may also be performed server-side bythe messaging server application 114, in view of the hardwarerequirements for such processing.

The application server 112 also includes the image processing system116, which is dedicated to performing various image processingoperations, typically with respect to images or video received withinthe payload of a message at the messaging server application 114.

The social network system 122 supports various social networkingfunctions and services, and makes these functions and services availableto the messaging server application 114. To this end, the social networksystem 122 maintains and accesses an entity graph (e.g., entity graph304 in FIG. 3 ) within the database 120. Examples of functions andservices supported by the social network system 122 include theidentification of other users of the messaging system 100 with whom aparticular user has relationships or whom the particular user is“following”, and also the identification of other entities and interestsof a particular user.

The application server 112 is communicatively coupled to a databaseserver 118, which facilitates access to a database 120 in which isstored data associated with messages processed by the messaging serverapplication 114.

FIG. 2 is block diagram illustrating further details regarding themessaging system 100, according to example embodiments. Specifically,the messaging system 100 is shown to comprise the messaging clientapplication 104 and the application server 112, which in turn embody anumber of subsystems, namely an ephemeral timer system 202, a collectionmanagement system 204, an annotation system 206, and a virtual objectmachine learning system 250. The virtual object machine learning system250 is discussed in further detail below.

Further, although FIG. 2 shows the virtual object machine learningsystem 250 integrated into the message client application 104. In someexample embodiments, the virtual object machine learning system 250 isintegrated entirely within application server 112. Further, in someexample embodiments, some of the engines of the virtual object machinelearning system 250 are executed on a server (e.g., application server112) and some of the engines of the virtual object machine learningsystem 250 are executed from the client device 102 (e.g., as part ofclient application 104). For example, an instance of the machinelearning engine 630 (discussed below) may be trained to create aclassifier model. The classifier model data can then be transferred toone or more client devices 102. On the one or more client devices 102,another instance of the machine learning engine 630 may apply thereceived classifier model to classify images captured on the respectiveclient devices 102.

The ephemeral timer system 202 is responsible for enforcing thetemporary access to content permitted by the messaging clientapplication 104 and the messaging server application 114. To this end,the ephemeral timer system 202 incorporates a number of timers that,based on duration and display parameters associated with a message orcollection of messages (e.g., a SNAPCHAT Story), selectively display andenable access to messages and associated content via the messagingclient application 104. Further details regarding the operation of theephemeral timer system 202 are provided below.

The collection management system 204 is responsible for managingcollections of media (e.g., collections of text, image, video, and audiodata). In some examples, a collection of content (e.g., messages,including images, video, text, and audio) may be organized into an“event gallery” or an “event story”. Such a collection may be madeavailable for a specified time period, such as the duration of an eventto which the content relates. For example, content relating to a musicconcert may be made available as a “story” for the duration of thatmusic concert. The collection management system 204 may also beresponsible for publishing an icon that provides notification of theexistence of a particular collection to the user interface of themessaging client application 104.

The collection management system 204 furthermore includes a curationinterface 208 that allows a collection manager to manage and curate aparticular collection of content. For example, the curation interface208 enables an event organizer to curate a collection of contentrelating to a specific event (e.g., delete inappropriate content orredundant messages). Additionally, the collection management system 204employs machine vision (or image recognition technology) and contentrules to automatically curate a content collection. In certainembodiments, compensation may be paid to a user for inclusion ofuser-generated content into a collection. In such cases, the curationinterface 208 operates to automatically make payments to such users forthe use of their content.

The annotation system 206 provides various functions that enable a userto annotate or otherwise modify or edit media content associated with amessage. For example, the annotation system 206 provides functionsrelated to the generation and publishing of media overlays for messagesprocessed by the messaging system 100. The annotation system 206operatively supplies a media overlay (e.g., a SNAPCHAT Geofilter orfilter) to the messaging client application 104 based on a geolocationof the client device 102. In another example, the annotation system 206operatively supplies a media overlay to the messaging client application104 based on other information, such as social network information ofthe user of the client device 102. A media overlay may include audio andvisual content and visual effects. Examples of audio and visual contentinclude pictures, texts, logos, animations, and sound effects. Anexample of a visual effect includes color overlaying. The audio andvisual content or the visual effects can be applied to a media contentitem (e.g., a photo) at the client device 102. For example, the mediaoverlay includes text that can be overlaid on top of a photographgenerated by the client device 102. In another example, the mediaoverlay includes an identification of a location (e.g., Venice Beach), aname of a live event, or a name of a merchant (e.g., Beach CoffeeHouse). In another example, the annotation system 206 uses thegeolocation of the client device 102 to identify a media overlay thatincludes the name of a merchant at the geolocation of the client device102. The media overlay may include other indicia associated with themerchant. The media overlays may be stored in the database 120 andaccessed through the database server 118.

In one example embodiment, the annotation system 206 provides auser-based publication platform that enables users to select ageolocation on a map, and upload content associated with the selectedgeolocation. The user may also specify circumstances under whichparticular content should be offered to other users. The annotationsystem 206 generates a media overlay that includes the uploaded contentand associates the uploaded content with the selected geolocation.

In another example embodiment, the annotation system 206 provides amerchant-based publication platform that enables merchants to select aparticular media overlay associated with a geolocation via a biddingprocess. For example, the annotation system 206 associates the mediaoverlay of a highest-bidding merchant with a corresponding geolocationfor a predefined amount of time

FIG. 3 is a schematic diagram illustrating data 300 which may be storedin the database 120 of the messaging server system 108, according tocertain example embodiments. While the content of the database 120 isshown to comprise a number of tables, it will be appreciated that thedata could be stored in other types of data structures (e.g., as anobject-oriented database).

The database 120 includes message data stored within a message table314. An entity table 302 stores entity data, including an entity graph304. Entities for which records are maintained within the entity table302 may include individuals, corporate entities, organizations, objects,places, events, etc. Regardless of type, any entity regarding which themessaging server system 108 stores data may be a recognized entity. Eachentity is provided with a unique identifier, as well as an entity typeidentifier (not shown).

The entity graph 304 furthermore stores information regardingrelationships and associations between or among entities. Suchrelationships may be social, professional (e.g., work at a commoncorporation or organization), interested-based, or activity-based,merely for example.

The database 120 also stores annotation data, in the example form offilters, in an annotation table 312. Filters for which data is storedwithin the annotation table 312 are associated with and applied tovideos (for which data is stored in a video table 310) and/or images(for which data is stored in an image table 308). Filters, in oneexample, are overlays that are displayed as overlaid on an image orvideo during presentation to a recipient user. Filters may be of varioustypes, including user-selected filters from a gallery of filterspresented to a sending user by the messaging client application 104 whenthe sending user is composing a message. Other types of filters includegeolocation filters (also known as geo-filters) which may be presentedto a sending user based on geographic location. For example, geolocationfilters specific to a neighborhood or special location may be presentedwithin a user interface by the messaging client application 104, basedon geolocation information determined by a Global Positioning System(GPS) unit of the client device 102. Another type of filter is a datafilter, which may be selectively presented to a sending user by themessaging client application 104, based on other inputs or informationgathered by the client device 102 during the message creation process.Examples of data filters include a current temperature at a specificlocation, a current speed at which a sending user is traveling, abattery life for a client device 102, or the current time.

Other annotation data that may be stored within the image table 308 isso-called “lens” data. A “lens” may be a real-time special effect andsound that may be added to an image or a video.

As mentioned above, the video table 310 stores video data which, in oneembodiment, is associated with messages for which records are maintainedwithin the message table 314. Similarly, the image table 308 storesimage data associated with messages for which message data is stored inthe message table 314. The entity table 302 may associate variousannotations from the annotation table 312 with various images and videosstored in the image table 308 and the video table 310.

A story table 306 stores data regarding collections of messages andassociated image, video, or audio data, which are compiled into acollection (e.g., a SNAPCHAT Story or a gallery). The creation of aparticular collection may be initiated by a particular user (e.g., eachuser for whom a record is maintained in the entity table 302). A usermay create a “personal story” in the form of a collection of contentthat has been created and sent/broadcast by that user. To this end, theuser interface of the messaging client application 104 may include anicon that is user-selectable to enable a sending user to add specificcontent to his or her personal story.

A collection may also constitute a “live story”, which is a collectionof content from multiple users that is created manually, automatically,or using a combination of manual and automatic techniques. For example,a “live story” may constitute a curated stream of user-submitted contentfrom various locations and events. Users whose client devices 102 havelocation services enabled and are at a common location or event at aparticular time may, for example, be presented with an option, via auser interface of the messaging client application 104, to contributecontent to a particular live story. The live story may be identified tothe user by the messaging client application 104, based on his or herlocation. The end result is a “live story” told from a communityperspective.

A further type of content collection is known as a “location story”,which enables a user whose client device 102 is located within aspecific geographic location (e.g., on a college or university campus)to contribute to a particular collection. In some embodiments, acontribution to a location story may require a second degree ofauthentication to verify that the end user belongs to a specificorganization or other entity (e.g., is a student on the universitycampus).

FIG. 4 is a schematic diagram illustrating a structure of a message 400,according to some embodiments, generated by a messaging clientapplication 104 for communication to a further messaging clientapplication 104 or the messaging server application 114. The content ofa particular message 400 is used to populate the message table 314stored within the database 120, accessible by the messaging serverapplication 114. Similarly, the content of a message 400 is stored inmemory as “in-transit” or “in-flight” data of the client device 102 orthe application server 112. The message 400 is shown to include thefollowing components:

-   -   A message identifier 402: a unique identifier that identifies        the message 400.    -   A message text payload 404: text, to be generated by a user via        a user interface of the client device 102, and that is included        in the message 400.    -   A message image payload 406: image data, captured by a camera        component of a client device 102 or retrieved from memory of a        client device 102, and that is included in the message 400.    -   A message video payload 408: video data, captured by a camera        component or retrieved from a memory component of the client        device 102, and that is included in the message 400.    -   A message audio payload 410: audio data, captured by a        microphone or retrieved from the memory component of the client        device 102, and that is included in the message 400.    -   Message annotations 412: annotation data (e.g., filters,        stickers, or other enhancements) that represents annotations to        be applied to the message image payload 406, message video        payload 408, or message audio payload 410 of the message 400.    -   A message duration parameter 414: a parameter value indicating,        in seconds, the amount of time for which content of the message        400 (e.g., the message image payload 406, message video payload        408, and message audio payload 410) is to be presented or made        accessible to a user via the messaging client application 104.    -   A message geolocation parameter 416: geolocation data (e.g.,        latitudinal and longitudinal coordinates) associated with the        content payload of the message 400. Multiple message geolocation        parameter 416 values may be included in the payload, each of        these parameter values being associated with respective content        items included in the content (e.g., a specific image in the        message image payload 406, or a specific video in the message        video payload 408).    -   A message story identifier 418: identifier values identifying        one or more content collections (e.g., “stories”) with which a        particular content item in the message image payload 406 of the        message 400 is associated. For example, multiple images within        the message image payload 406 may each be associated with        multiple content collections using identifier values.    -   A message tag 420: one or more tags, each of which is indicative        of the subject matter of content included in the message        payload. For example, where a particular image included in the        message image payload 406 depicts an animal (e.g., a lion), a        tag value may be included within the message tag 420 that is        indicative of the relevant animal. Tag values may be generated        manually, based on user input, or may be automatically generated        using, for example, image recognition.    -   A message sender identifier 422: an identifier (e.g., a        messaging system identifier, email address, or device        identifier) indicative of a user of the client device 102 on        which the message 400 was generated and from which the message        400 was sent.    -   A message receiver identifier 424: an identifier (e.g., a        messaging system identifier, email address, or device        identifier) indicative of a user of the client device 102 to        which the message 400 is addressed.

The contents (e.g., values) of the various components of the message 400may be pointers to locations in tables within which content data valuesare stored. For example, an image value in the message image payload 406may be a pointer to (or address of) a location within the image table308. Similarly, values within the message video payload 408 may point todata stored within the video table 310, values stored within the messageannotations 412 may point to data stored in the annotation table 312,values stored within the message story identifier 418 may point to datastored in the story table 306, and values stored within the messagesender identifier 422 and the message receiver identifier 424 may pointto user records stored within the entity table 302.

FIG. 5 is a schematic diagram illustrating an access-limiting process500, in terms of which access to content (e.g., an ephemeral message502, and associated multimedia payload of data) or a content collection(e.g., an ephemeral message story 504) may be time-limited (e.g., madeephemeral).

An ephemeral message 502 is shown to be associated with a messageduration parameter 506, the value of which determines an amount of timethat the ephemeral message 502 will be displayed to a receiving user ofthe ephemeral message 502 by the messaging client application 104. Inone embodiment, where the messaging client application 104 is a SNAPCHATapplication client, an ephemeral message 502 is viewable by a receivinguser for up to a maximum of seconds, depending on the amount of timethat the sending user specifies using the message duration parameter506.

The message duration parameter 506 and the message receiver identifier424 are shown to be inputs to a message timer 512, which is responsiblefor determining the amount of time that the ephemeral message 502 isshown to a particular receiving user identified by the message receiveridentifier 424. In particular, the ephemeral message 502 will only beshown to the relevant receiving user for a time period determined by thevalue of the message duration parameter 506. The message timer 512 isshown to provide output to a more generalized ephemeral timer system202, which is responsible for the overall timing of display of content(e.g., an ephemeral message 502) to a receiving user.

The ephemeral message 502 is shown in FIG. 5 to be included within anephemeral message story 504 (e.g., a personal SNAPCHAT Story, or anevent story). The ephemeral message story 504 has an associated storyduration parameter 508, a value of which determines a time duration forwhich the ephemeral message story 504 is presented and accessible tousers of the messaging system 100. The story duration parameter 508, forexample, may be the duration of a music concert, where the ephemeralmessage story 504 is a collection of content pertaining to that concert.Alternatively, a user (either the owning user or a curator user) mayspecify the value for the story duration parameter 508 when performingthe setup and creation of the ephemeral message story 504.

Additionally, each ephemeral message 502 within the ephemeral messagestory 504 has an associated story participation parameter 510, a valueof which determines the duration of time for which the ephemeral message502 will be accessible within the context of the ephemeral message story504. Accordingly, a particular ephemeral message 502 may “expire” andbecome inaccessible within the context of the ephemeral message story504, prior to the ephemeral message story 504 itself expiring in termsof the story duration parameter 508. The story duration parameter 508,story participation parameter 510, and message receiver identifier 424each provide input to a story timer 514, which operationally determineswhether a particular ephemeral message 502 of the ephemeral messagestory 504 will be displayed to a particular receiving user and, if so,for how long. Note that the ephemeral message story 504 is also aware ofthe identity of the particular receiving user as a result of the messagereceiver identifier 424.

Accordingly, the story timer 514 operationally controls the overalllifespan of an associated ephemeral message story 504, as well as anindividual ephemeral message 502 included in the ephemeral message story504. In one embodiment, each and every ephemeral message 502 within theephemeral message story 504 remains viewable and accessible for a timeperiod specified by the story duration parameter 508. In a furtherembodiment, a certain ephemeral message 502 may expire, within thecontext of the ephemeral message story 504, based on a storyparticipation parameter 510. Note that a message duration parameter 506may still determine the duration of time for which a particularephemeral message 502 is displayed to a receiving user, even within thecontext of the ephemeral message story 504. Accordingly, the messageduration parameter 506 determines the duration of time that a particularephemeral message 502 is displayed to a receiving user, regardless ofwhether the receiving user is viewing that ephemeral message 502 insideor outside the context of an ephemeral message story 504.

The ephemeral timer system 202 may furthermore operationally remove aparticular ephemeral message 502 from the ephemeral message story 504based on a determination that it has exceeded an associated storyparticipation parameter 510. For example, when a sending user hasestablished a story participation parameter 510 of 24 hours fromposting, the ephemeral timer system 202 will remove the relevantephemeral message 502 from the ephemeral message story 504 after thespecified 24 hours. The ephemeral timer system 202 also operates toremove an ephemeral message story 504 either when the storyparticipation parameter 510 for each and every ephemeral message 502within the ephemeral message story 504 has expired, or when theephemeral message story 504 itself has expired in terms of the storyduration parameter 508.

In certain use cases, a creator of a particular ephemeral message story504 may specify an indefinite story duration parameter 508. In thiscase, the expiration of the story participation parameter 510 for thelast remaining ephemeral message 502 within the ephemeral message story504 will determine when the ephemeral message story 504 itself expires.In this case, a new ephemeral message 502, added to the ephemeralmessage story 504, with a new story participation parameter 510,effectively extends the life of an ephemeral message story 504 to equalthe value of the story participation parameter 510.

In response to the ephemeral timer system 202 determining that anephemeral message story 504 has expired (e.g., is no longer accessible),the ephemeral timer system 202 communicates with the messaging system100 (e.g., specifically, the messaging client application 104) to causean indicium (e.g., an icon) associated with the relevant ephemeralmessage story 504 to no longer be displayed within a user interface ofthe messaging client application 104. Similarly, when the ephemeraltimer system 202 determines that the message duration parameter 506 fora particular ephemeral message 502 has expired, the ephemeral timersystem 202 causes the messaging client application 104 to no longerdisplay an indicium (e.g., an icon or textual identification) associatedwith the ephemeral message 502.

FIG. 6 shows a functional architecture for a virtual object machinelearning system 250, according to some example embodiments. Asillustrated, the virtual object machine learning system 250 comprises aninterface engine 605, a model engine 610, a gesture engine 615, a modeltexture engine 620, an environment engine 625, a machine learning engine630, and a render engine 635.

The interface engine 605 is configured to receive images forclassification. For example, the interface engine 605 may receive imageor video data captured from an image capture sensor of the client device102. The interface engine 605 may convey the image or video data to amachine learning scheme (e.g., machine learning engine 630) to detect agesture of a hand depicted in the image or video data. The annotationsystem 206 may overlay content (e.g., message annotations 412) on thevideo or image data based on the detected hand gesture. For example, ifthe image or video data depicts a person making a peace sign withhis/her fingers, the annotation system 206 may overlay words, such as“One Love” on the image or video data. The annotated message may then bepublished via social network system 122 for other users to view,according to some example embodiments.

The model engine 610 manages 3D model data of a subject to beclassified. For example, the model engine 610 may manage 3D models ofdifferent sizes of hand, 3D models of different types of dogs (e.g.,different breeds, ages) and so forth. The model engine 610 may selectone of the 3D models to create a set of training data (e.g., images of aLabrador retriever) for training in one iteration, and another model foranother set of training data (e.g., images of a pug) in another. Eachmodel may be arranged in different poses, with different background andother variations to create a rich set of training data.

The gesture engine 615 is configured to change the arrangement of given3D model. For example, the gesture engine 615 may programmaticallyarrange a model of a hand into a peace sign for one set of trainingimages and then programmatically arrange the model of the hand into athumbs-up gesture for another set of training images.

The model texture engine 620 is configured to change the texture of agiven 3D model. For example, the model texture engine 620 may apply abrown texture to a Labrador retriever to create a set of images for achocolate Labrador retriever, and then apply a black texture to aLabrador model to create another set of training images for a blackLabrador retriever.

Then environment engine 625 is configured to control the 3D environmentsurrounding a given 3D model. For example, the environment engine 625may programmatically change the texture of a spherical background thatsurrounds the 3D model. The texture may be a photo image of a real worldenvironment such as a 360 image of the Grand Canyon in Arizona or alow-lit living room. The environment engine 625 is further configured tomanage the lighting and virtual camera placements, according to someexample embodiments.

The machine learning engine 630 is configured to train a machinelearning scheme (e.g., support vector machine (SVM) scheme, logisticregression scheme, K-nearest neighbor scheme, convolutional neuralnetworks) on the training data created from the 3D models. The machinelearning engine 630 is further configured to receive an image or videodata from the interface engine 605 and use the trained model to classifythe type of object being depicted (e.g., dog, cat, human hand), andfurther classify the pose or gesture of the object being depicted in theimage or video data.

The render engine 635 is configured to generate the training data byrendering the 3D model in the 3D environment, according to some exampleembodiments. The render engine 635 may further include instructions toloop over different types of models, gestures, model textures, andenvironment variables (e.g., textures, lighting, camera placements) togenerate a multitude of combinations of training images, as discussed infurther detail with reference to FIG. 8 . The render engine 635 isfurther configured to store the training images in a memory locationaccessible to the training machine learning engine 630, according tosome example embodiments.

FIG. 7 shows a flow diagram of a method 700 for implementing virtualobject machine learning, according to some example embodiments. Atoperation 705, the render engine 635 generates training images from oneor more 3D models. As discussed, the training images can be made bycreated renders of a 3D model having marker metadata. The 3D model canbe rendered in different poses, in different lighting settings, and withdifferent textures applied to the model and the background. Theinterplay of the different textures creates a unique set of images thatthe machine learning engine scheme can use to train itself to identifyobjects in a wide variety of scenarios.

At operation 710, machine learning engine 630 trains a machine learningscheme on the training images to generate a classifier model. Themachine learning scheme can then classify objects by applying thetrained classifier model on the objects. The output of the machinelearning scheme is a numerical likelihood that the depicted object is ofa given type, according to some example embodiments. In some exampleembodiments, the machine learning engine 630 is configured to use asupervised learning scheme (a learning scheme that generally requireslabeled datasets) for training and classification. Example schemesinclude a support vector machine scheme, a K-nearest neighbor scheme, asupervised neural network (e.g., a neural network trained using labeleddata markers), and other types of supervised schemes. Further, in someembodiments, the machine learning engine 630 may implement anon-supervised machine learning scheme such as k-means clustering andunsupervised neural networks.

At operation 715, the interface engine 605 receives image data (e.g.image or video data). For example, a client device 102 (e.g.,smartphone) may capture an image of a hand and input the capture intothe interface engine 605 for classification. At operation 720, themachine learning engine 630 identifies or otherwise detects key pointsof the object depicted in the received image. Example key points includejoints, edges, intersections, prominences, and other visual dataindicators that can be used to characterize an object.

At operation 725, the machine learning engine 630 detects object datausing the key points. In some example embodiments, at operation 725 themachine learning engine 630 uses the key points to classify the objectas being in a certain pose (e.g., a gesture). In some exampleembodiments, at operation 725 the machine learning engine 630 uses thekeypoints to output pixel masking data for a depicted object. The pixelmasking system can use the keypoints to more readily label parts of thedepicted subject as one type area (e.g., a person's arms and legs)) oranother type of area (e.g., the torso of the same person). For example,at operation 725, if the keypoints correspond to an arm, the surroundingarea is masked as an arm area. Whereas if the keypoints belong to atorso area, the surrounding area is masked (labeled) as a torso area.Further details of an example approaches using image features aredescribed in: U.S. Patent Application Ser. No. 62/481,415, titled“GENERATING A PIXEL MASK USING MACHINE LEARNING”, Attorney Docket No.4218.463PRV, filed on Apr. 4, 2017, which is hereby incorporated byreference in entirety. In some example embodiments, the location of thekey points is output or otherwise stored in memory for use in anothersystem (e.g., an augmented reality system), and operation 725 is notperformed.

FIG. 8 shows a flow diagram of a method 800 for generating trainingdata, according to some example embodiments. Although the method 800 isillustrated as three nested loop operations, it is appreciated that thedifferent combinations can be achieved using other flow logic. Further,in some example embodiments, some of the operations may be completedmanually instead of programmatically. For example, a human user maychange the gesture of the 3D model manually, then apply the programmaticloops to the arranged 3D model to generate a set of training images. Atoperation 805, the model engine 610 identifies a 3D model of an object.For example, the model engine 610 identifies a 3D model of a hand (e.g.,a 3D model file). At 810, the gesture engine 615 arranges the objectinto a gesture or pose. The gesture engine 615 may arrange the objectinto a pose within a 3D environment (e.g., application that creates a 3Denvironment in which a model can be arranged in virtual space). Atoperation 815, the model texture engine 620 applies a model texture tothe model. The model texture may be the first of a collection. Forexample, the collection may be a collection of fur types and colors. Atoperation 820, environment engine 625 applies environment variables(e.g. background texture, lighting position and intensity, virtualcamera placement). Examples of environment variables are furtherdiscussed below with reference to FIGS. 9 and 11 .

At operation 825, the environment engine 625 determines whether thereare additional sets of environment variables to apply. If there areadditional sets of environment variables to apply, the method continuesto operation 830. A different combination of variables may includechanging the background image but keeping the camera positions, orchanging the camera positions and the background. At operation 830, theenvironment engine 625 identifies a next set of environment variables toapply. The process then loops back to operation 820, where theenvironment engine 625 applies the new set of environment variables.Although not illustrated in FIG. 8 , for every combination of variables,one or more virtual cameras may capture and store renders of the 3Dmodel in the virtual environment.

Continuing, assuming there are no further environment variables, themethod 800 continues from operation 825 to operation 835. At operation835, the model texture engine 620 determines whether there areadditional textures to apply to the model. If there are additionaltextures to apply to the model, the model texture engine 620 identifiesthe next object texture to apply to the model at operation 840. Themethod then continues to operation 815 where the model texture engine620 applies the next texture to the model. The model with the newtexture is then imaged with all of the backgrounds for the environmentvariables loop (operations 820, 825, and 830) as a nested loopoperation.

Continuing from operation 835, assuming there are no additional objecttextures to apply to the model, the method 800 proceeds to operation845, with a gesture engine 615 determines whether there are additionalgestures to apply to the model. If there are more gestures to apply tothe model, the method 800 continues to operation 850, where the nextobject gesture is identified. From operation 815 the method 800continues to operation 810, where the gesture engine 615 applies the newgesture to the 3D model object. From operation 810, the method 800continues to the nested loop operations of the object model textures andthe nested loop of the environment variables. Returning to operation845, if the there are no additional object gestures then the method 800continues to operation 855, which may be an exit of the subroutine orstorage of the training data.

FIG. 9 shows an example rendering environment 900 for generatingtraining images, according to some example embodiments. As illustrated,a 3D hand 905 is a virtual 3D model surrounded by a 3D environment 910that curves around the 3D hand 905. In some example embodiments, the 3Denvironment 910 is a spherical portion or a complete sphere, with the 3Dhand 905 inside the curving portion (e.g., inside the sphere). In someexample embodiments, the background may be in other shapes, such as aflat plane. As depicted, the 3D hand 905 has been arranged into an“okay” sign gesture or pose. While in the gesture, one or more virtualcameras 915-935 may capture a rendering (e.g., output a 2-D image file)of the 3D hand 905 from different positions. The rendered 3D hand fromdifferent positions can be stored as different image files. The 3Denvironment may further have virtual light sources (not depicted) and atexture, which can be used to set light sources as discussed in furtherdetail below with reference to FIG. 11 .

FIG. 10 shows an example structure of a 3D hand 1000, according to someexample embodiments. The 3D hand 1000 may be generated using anunderlying rigging structure 1005 (e.g., bones, skeleton). The keypoints are regions of interest (ROI) of a model that can be used todetermine a 3D model's pose or gesture. For example, in the 3D hand 1000the joints between different segments of the rigging structure 1005 canbe used to determine that the 3D hand 1000 is making the “okay” signgesture. The key points can be tracked using markers which are indicatedin FIG. 10 by black solid circles. For example, the middle finger middlejoint marker 1010 is black circle at the interface of two segments thatcorrespond to one of the middle finger's knuckles. Other markers arelikewise indicated in FIG. 10 . The coordinates of the ROI markers inthe image are stored as metadata with the 3D hand model. Further, when a2D render of the 3D hand is created, the location of the markers can bestored as metadata to the 2D render or as a separate file thatreferences the 2D render file. In some example embodiments, thekeypoints are indicated using polygons that circumscribe or otherwiseindicate a given type of area. For example, a pinky finger can be a keypoint indicated by a ROI polygon marker 1015. Other areas (e.g.,fingers, palms, the entire hand) can be likewise tracked using ROIpolygon markers. As discussed, the coordinates data (e.g., X any Yposition within a given image of the markers (e.g., point based markersto indicate joints, polygons to indicate areas) can be stored asmetadata for given image. The machine learning schemes can use thecoordinate data as labels for supervised learning.

Markers can indicate key points other than joints, according to someexample embodiments. For example, if a 3D model is a human face, themarkers can indicate corners of the mouth, corners of the eyes, edges(e.g., the outline of a human lip, the outline of a dog, etc.), orprominences (e.g., high cheek bones).

FIG. 11 shows an environment texture 1100 for use as an environmentbackground, according to some example embodiments. As illustrated, theexample texture is a spherical image (360° panorama) image of a realworld environment (e.g., a posh living room). The depicted environmenttexture 1100 includes floor-to-ceiling windows 1105, walls 1110, a floor1115, and a ceiling 1120. The environment texture 1100 can be generatedas a high dynamic range (HDR) image, which combines multiple imagecaptures to capture very light lights and very dark darks, as inunderstood in the art. Spherical HDR images can be generated using acamera settings or through post-production software (e.g., AdobePhotoshop®).

Although not depicted, sunlight is shining in through the windows 1105thereby making the pixels that that correspond to the windows 1105 alighter shade than the pixels that correspond to the walls 1110, thefloor 1115, and the ceiling 1120. The environment texture 1100 (e.g.,the HDR image of the living room), mapped to the 3D environment 910 (thesphere). The render engine 635 can be configured to use a ray tracingscheme (e.g., Mental Ray) to create lighting and reflection effectsbetween the 3D model and the surrounding environment. That is, the 3Dmodel may have a given texture (e.g., white skin, freckles) with setreflection values which reflect virtual rays emanating from lightsources of the background (e.g., windows 1105).

The environment texture 1100 can be switched to create renders of the 3Dmodel in different environments. For example, the environment texture1100 may be a HDR image of the Grand Canyon. Thus, when the 3D rendersare generated as training images, the 3D renders capture how the 3D handwould look in the simulated Grand Canyon. Then, according to someexample embodiments, the texture of the 3D model can be switched (e.g.,switching from a lighter shade skin to a darker shade skin), then againimaged in the Grand Canyon to capture how the 3D hand of a darker shadewould look in the Grand Canyon. Further, the environment texture 1100can then be switched to a different lighting environment to capture thehand (e.g., with the lighter shade skin applied, with the darker shadeskin texture applied) in a different real-world environment, such as anight club (e.g., the Las Vegas Strip, inside a nightclub). In this way,different detailed training images can be created and the machinelearning engine 630 can be trained on the rich set of training imageswithout labeling the markers, without having to control the model (e.g.,dog) to display different poses, and doing so in different environments(e.g., different lighting scenarios, different camera angles).

FIG. 12 shows example gestures of a 3D hand model, according to someexample embodiments. The examples include a hand making a thumbs upgesture 1200, the hand making a rock-on gesture (also known as the TexasLonghorns sign), and a hand making a crossed-fingers gesture. Asdiscussed, each hand can have markers that indicate key points (e.g.,joints, tips of fingers) that can be automatically detected to train themachine learning engine 630. Each of the hands can also have differenttextures for different skin types, etc. Further, each of the hands withthe different textures can be imaged (e.g., rendered) from differentangles (e.g., from different virtual cameras 915-935 at differentpositions) in front of different backgrounds (e.g., spherical HDRimages) that have different lighting effects. The classification modelcreated by the machine learning engine 630 can then be applied to a widerange of images to more correctly classify gestures or poses of depictedsubjects.

FIG. 13 shows example poses of a 3D Labrador retriever, according tosome example embodiments. The examples include the depicted dog in aplayful pose 1300, the dog in an upright aggression pose 1305 with itstail erect, and the dog in a crouched pose with its tail down 1310. Asdiscussed, each 3D model of the dog can have markers that indicate keypoints (e.g., feet location, eye location, tail location) that themachine learning engine 630 automatically identify to train itsclassification model. Each of the 3D dogs can also have differenttextures for different colors, fur types, fur patterns, fur hair cutdesigns. Further, each of the 3D dogs with the different textures can beimaged (e.g., rendered) from different angles (e.g., from differentvirtual cameras 915-935 at different positions) in front of differentbackgrounds (e.g., spherical HDR images) that have different lightingeffects. In this way, the classification model created by the machinelearning engine 630 can distinguish between dissimilar poses (e.g., pose1300 where the dog is somewhat crouching, and pose 1305, where the dogis upright) and similar poses (e.g., pose 1300 where the dog iscrouching but playful, and pose 1310 where the dog is crouching butlikely aggressive).

FIG. 14 shows a client device implementing the virtual object machinelearning system 250, according to some example embodiments. In theexample of FIG. 14 , an image capture device on the back side of theclient device 1400 has captured an image 1405 of a Labrador retriever.The machine learning engine 630 has previously been trained on the richset of training images as discussed above. In some example embodiments,responsive to receiving the captured image, the machine learning engine630 applies its trained classification model to the image 1405 todetermine that the dog is in a playful pose. Responsive to thedetermination, the annotation system 206 overlays text 1410 thatcorresponds to the pose of the dog. For example, the annotation system206 may overlay “Who's a good boy!?” on the image 1405. The image 1405with the text 1410 can then be stored as a single image (e.g., mergingthe layers) and published online (e.g. to the social media system) asdiscussed above.

FIG. 15 is a block diagram illustrating an example software architecture1506, which may be used in conjunction with various hardwarearchitectures herein described. FIG. 15 is a non-limiting example of asoftware architecture, and it will be appreciated that many otherarchitectures may be implemented to facilitate the functionalitydescribed herein. The software architecture 1506 may execute on hardwaresuch as a machine 1600 of FIG. 16 that includes, among other things,processors 1610, memory 1632, and I/O components 1650. A representativehardware layer 1552 is illustrated and can represent, for example, themachine 1600 of FIG. 16 . The representative hardware layer 1552includes a processing unit 1554 having associated executableinstructions 1504. The executable instructions 1504 represent theexecutable instructions of the software architecture 1506, includingimplementation of the methods, components, and so forth describedherein. The hardware layer 1552 also includes memory and/or storagemodules memory/storage 1556, which also have the executable instructions1504. The hardware layer 1552 may also comprise other hardware 1558.

In the example architecture of FIG. 15 , the software architecture 1506may be conceptualized as a stack of layers where each layer providesparticular functionality. For example, the software architecture 1506may include layers such as an operating system 1502, libraries 1520,frameworks/middleware 1518, applications 1516, and a presentation layer1514. Operationally, the applications 1516 and/or other componentswithin the layers may invoke application programming interface (API)calls 1508 through the software stack and receive a response in the formof messages 1512. The layers illustrated are representative in natureand not all software architectures have all layers. For example, somemobile or special-purpose operating systems may not provide aframeworks/middleware 1518, while others may provide such a layer. Othersoftware architectures may include additional or different layers.

The operating system 1502 may manage hardware resources and providecommon services. The operating system 1502 may include, for example, akernel 1522, services 1524, and drivers 1526. The kernel 1522 may act asan abstraction layer between the hardware and the other software layers.For example, the kernel 1522 may be responsible for memory management,processor management (e.g., scheduling), component management,networking, security settings, and so on. The services 1524 may provideother common services for the other software layers. The drivers 1526are responsible for controlling or interfacing with the underlyinghardware. For instance, the drivers 1526 include display drivers, cameradrivers, Bluetooth® drivers, flash memory drivers, serial communicationdrivers (e.g., Universal Serial Bus (USB) drivers), Wi-Fi® drivers,audio drivers, power management drivers, and so forth depending on thehardware configuration.

The libraries 1520 provide a common infrastructure that is used by theapplications 1515 and/or other components and/or layers. The libraries1520 provide functionality that allows other software components toperform tasks in an easier fashion than by interfacing directly with theunderlying operating system 1502 functionality (e.g., kernel 1522,services 1524, and/or drivers 1526). The libraries 1520 may includesystem libraries 1544 (e.g., C standard library) that may providefunctions such as memory allocation functions, string manipulationfunctions, mathematical functions, and the like. In addition, thelibraries 1520 may include API libraries 1546 such as media libraries(e.g., libraries to support presentation and manipulation of variousmedia formats such as MPEG4, H.264, MP3, AAC, AMR, JPG, or PNG),graphics libraries (e.g., an OpenGL framework that may be used to render2D and 3D graphic content on a display), database libraries (e.g.,SQLite that may provide various relational database functions), weblibraries (e.g., WebKit that may provide web browsing functionality),and the like. The libraries 1520 may also include a wide variety ofother libraries 1548 to provide many other APIs to the applications 1515and other software components/modules.

The frameworks/middleware 1518 provide a higher-level commoninfrastructure that may be used by the applications 1515 and/or othersoftware components/modules. For example, the frameworks/middleware 1518may provide various graphic user interface (GUI) functions, high-levelresource management, high-level location services, and so forth. Theframeworks/middleware 1518 may provide a broad spectrum of other APIsthat may be utilized by the applications 1515 and/or other softwarecomponents/modules, some of which may be specific to a particularoperating system 1502 or platform.

The applications 1515 include built-in applications 1538 and/orthird-party applications 1540. Examples of representative built-inapplications 1538 may include, but are not limited to, a contactsapplication, a browser application, a book reader application, alocation application, a media application, a messaging application,and/or a game application. The third-party applications 1540 may includean application developed using the ANDROID™ or IOS™ software developmentkit (SDK) by an entity other than the vendor of the particular platform,and may be mobile software running on a mobile operating system such asIOS™, ANDROID™, WINDOWS® Phone, or other mobile operating systems. Thethird-party applications 1540 may invoke the API calls 1508 provided bythe mobile operating system (such as the operating system 1502) tofacilitate functionality described herein.

The applications 1515 may use built-in operating system functions (e.g.,kernel 1522, services 1524, and/or drivers 1526), libraries 1520, andframeworks/middleware 1518 to create user interfaces to interact withusers of the system. Alternatively, or additionally, in some systemsinteractions with a user may occur through a presentation layer, such asthe presentation layer 1514. In these systems, the application/component“logic” can be separated from the aspects of the application/componentthat interact with a user.

FIG. 16 is a block diagram illustrating components of a machine 1600,according to some example embodiments, able to read instructions from amachine-readable medium (e.g., a machine-readable storage medium) andperform any one or more of the methodologies discussed herein.Specifically, FIG. 16 shows a diagrammatic representation of the machine1600 in the example form of a computer system, within which instructions1616 (e.g., software, a program, an application, an applet, an app, orother executable code) for causing the machine 1600 to perform any oneor more of the methodologies discussed herein may be executed. As such,the instructions 1616 may be used to implement modules or componentsdescribed herein. The instructions 1616 transform the general,non-programmed machine 1600 into a particular machine 1600 programmed tocarry out the described and illustrated functions in the mannerdescribed. In alternative embodiments, the machine 1600 operates as astandalone device or may be coupled (e.g., networked) to other machines.In a networked deployment, the machine 1600 may operate in the capacityof a server machine or a client machine in a server-client networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The machine 1600 may comprise, but not be limitedto, a server computer, a client computer, a personal computer (PC), atablet computer, a laptop computer, a netbook, a set-top box (STB), apersonal digital assistant (PDA), an entertainment media system, acellular telephone, a smartphone, a mobile device, a wearable device(e.g., a smart watch), a smart home device (e.g., a smart appliance),other smart devices, a web appliance, a network router, a networkswitch, a network bridge, or any machine capable of executing theinstructions 1616, sequentially or otherwise, that specify actions to betaken by the machine 1600. Further, while only a single machine 1600 isillustrated, the term “machine” shall also be taken to include acollection of machines that individually or jointly execute theinstructions 1616 to perform any one or more of the methodologiesdiscussed herein.

The machine 1600 may include processors 1610 having individualprocessors 1612 and 1614 (e.g., cores), memory/storage 1630, and I/Ocomponents 1650, which may be configured to communicate with each othersuch as via a bus 1602. The memory/storage 1630 may include a memory1632, such as a main memory, or other memory storage, and a storage unit1636, both accessible to the processors 1610 such as via the bus 1602.The storage unit 1636 and memory 1630 store the instructions 1616embodying any one or more of the methodologies or functions describedherein. The instructions 1616 may also reside, completely or partially,within the memory 1632, within the storage unit 1636, within at leastone of the processors 1610 (e.g., within the processor's cache memory),or any suitable combination thereof, during execution thereof by themachine 1600. Accordingly, the memory 1632, the storage unit 1636, andthe memory of the processors 1610 are examples of machine-readablemedia.

The I/O components 1650 may include a wide variety of components toreceive input, provide output, produce output, transmit information,exchange information, capture measurements, and so on. The specific I/Ocomponents 1650 that are included in a particular machine 1600 willdepend on the type of machine. For example, portable machines such asmobile phones will likely include a touch input device or other suchinput mechanisms, while a headless server machine will likely notinclude such a touch input device. It will be appreciated that the I/Ocomponents 1650 may include many other components that are not shown inFIG. 16 . The I/O components 1650 are grouped according to functionalitymerely for simplifying the following discussion and the grouping is inno way limiting. In various example embodiments, the I/O components 1650may include output components 1652 and input components 1654. The outputcomponents 1652 may include visual components (e.g., a display such as aplasma display panel (PDP), a light-emitting diode (LED) display, aliquid-crystal display (LCD), a projector, or a cathode ray tube (CRT)),acoustic components (e.g., speakers), haptic components (e.g., avibratory motor, resistance mechanisms), other signal generators, and soforth. The input components 1654 may include alphanumeric inputcomponents (e.g., a keyboard, a touch screen configured to receivealphanumeric input, a photo-optical keyboard, or other alphanumericinput components), point-based input components (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, or other pointinginstruments), tactile input components (e.g., a physical button, a touchscreen that provides location and/or force of touches or touch gestures,or other tactile input components), audio input components (e.g., amicrophone), and the like.

In further example embodiments, the I/O components 1650 may includebiometric components 1656, motion components 1658, environmentcomponents 1660, or position components 1662 among a wide array of othercomponents. For example, the biometric components 1656 may includecomponents to detect expressions (e.g., hand expressions, facialexpressions, vocal expressions, body gestures, or eye tracking), measurebiosignals (e.g., blood pressure, heart rate, body temperature,perspiration, or brain waves), identify a person (e.g., voiceidentification, retinal identification, facial identification,fingerprint identification, or electroencephalogram-basedidentification), and the like. The motion components 1658 may includeacceleration sensor components (e.g., accelerometer), gravitation sensorcomponents, rotation sensor components (e.g., gyroscope), and so forth.The environment components 1660 may include, for example, illuminationsensor components (e.g., photometer), temperature sensor components(e.g., one or more thermometers that detect ambient temperature),humidity sensor components, pressure sensor components (e.g.,barometer), acoustic sensor components (e.g., one or more microphonesthat detect background noise), proximity sensor components (e.g.,infrared sensors that detect nearby objects), gas sensors (e.g., gassensors to detect concentrations of hazardous gases for safety or tomeasure pollutants in the atmosphere), or other components that mayprovide indications, measurements, or signals corresponding to asurrounding physical environment. The position components 1662 mayinclude location sensor components (e.g., a GPS receiver component),altitude sensor components (e.g., altimeters or barometers that detectair pressure from which altitude may be derived), orientation sensorcomponents (e.g., magnetometers), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 1650 may include communication components 1664operable to couple the machine 1600 to a network 1680 or devices 1670via a coupling 1682 and a coupling 1672 respectively. For example, thecommunication components 1664 may include a network interface componentor other suitable device to interface with the network 1680. In furtherexamples, the communication components 1664 may include wiredcommunication components, wireless communication components, cellularcommunication components, near field communication (NFC) components,Bluetooth® components (e.g., Bluetooth® Low Energy), Wi-Fi® components,and other communication components to provide communication via othermodalities. The devices 1670 may be another machine or any of a widevariety of peripheral devices (e.g., a peripheral device coupled via aUniversal Serial Bus (USB)).

Moreover, the communication components 1664 may detect identifiers orinclude components operable to detect identifiers. For example, thecommunication components 1664 may include radio frequency identification(RFID) tag reader components, NFC smart tag detection components,optical reader components (e.g., an optical sensor to detectone-dimensional barcodes such as Universal Product Code (UPC) barcode,multi-dimensional barcodes such as Quick Response (QR) code, Aztec code,Data Matrix, Dataglyph, MaxiCode, PDF416, Ultra Code, UCC RSS-2Dbarcode, and other optical codes), or acoustic detection components(e.g., microphones to identify tagged audio signals). In addition, avariety of information may be derived via the communication components1664, such as location via Internet Protocol (IP) geolocation, locationvia Wi-Fi® signal triangulation, location via detecting an NFC beaconsignal that may indicate a particular location, and so forth.

Glossary

“CARRIER SIGNAL” in this context refers to any intangible medium that iscapable of storing, encoding, or carrying instructions for execution bythe machine, and includes digital or analog communications signals orother intangible media to facilitate communication of such instructions.Instructions may be transmitted or received over the network using atransmission medium via a network interface device and using any one ofa number of well-known transfer protocols.

“CLIENT DEVICE” in this context refers to any machine that interfaces toa communications network to obtain resources from one or more serversystems or other client devices. A client device may be, but is notlimited to, a mobile phone, desktop computer, laptop, PDA, smartphone,tablet, ultrabook, netbook, multi-processor system, microprocessor-basedor programmable consumer electronics system, game console, set-top box,or any other communication device that a user may use to access anetwork.

“COMMUNICATIONS NETWORK” in this context refers to one or more portionsof a network that may be an ad hoc network, an intranet, an extranet, avirtual private network (VPN), a local area network (LAN), a wirelessLAN (WLAN), a wide area network (WAN), a wireless WAN (WWAN), ametropolitan area network (MAN), the Internet, a portion of theInternet, a portion of the Public Switched Telephone Network (PSTN), aplain old telephone service (POTS) network, a cellular telephonenetwork, a wireless network, a Wi-Fi® network, another type of network,or a combination of two or more such networks. For example, a network ora portion of a network may include a wireless or cellular network andthe coupling may be a Code Division Multiple Access (CDMA) connection, aGlobal System for Mobile communications (GSM) connection, or anothertype of cellular or wireless coupling. In this example, the coupling mayimplement any of a variety of types of data transfer technology, such asSingle Carrier Radio Transmission Technology (1×RTT), Evolution-DataOptimized (EVDO) technology, General Packet Radio Service (GPRS)technology, Enhanced Data rates for GSM Evolution (EDGE) technology,third Generation Partnership Project (3GPP) including 3G, fourthgeneration wireless (4G) networks, Universal Mobile TelecommunicationsSystem (UMTS), High-Speed Packet Access (HSPA), WorldwideInteroperability for Microwave Access (WiMAX), Long-Term Evolution (LTE)standard, others defined by various standard-setting organizations,other long-range protocols, or other data transfer technology.

“EPHEMERAL MESSAGE” in this context refers to a message that isaccessible for a time-limited duration. An ephemeral message 502 may bea text, an image, a video, and the like. The access time for theephemeral message 502 may be set by the message sender. Alternatively,the access time may be a default setting or a setting specified by therecipient. Regardless of the setting technique, the message istransitory.

“MACHINE-READABLE MEDIUM” in this context refers to a component, adevice, or other tangible media able to store instructions and datatemporarily or permanently and may include, but is not limited to,random-access memory (RAM), read-only memory (ROM), buffer memory, flashmemory, optical media, magnetic media, cache memory, other types ofstorage (e.g., erasable programmable read-only memory (EPROM)), and/orany suitable combination thereof. The term “machine-readable medium”should be taken to include a single medium or multiple media (e.g., acentralized or distributed database, or associated caches and servers)able to store instructions. The term “machine-readable medium” shallalso be taken to include any medium, or combination of multiple media,that is capable of storing instructions (e.g., code) for execution by amachine, such that the instructions, when executed by one or moreprocessors of the machine, cause the machine to perform any one or moreof the methodologies described herein. Accordingly, a “machine-readablemedium” refers to a single storage apparatus or device, as well as“cloud-based” storage systems or storage networks that include multiplestorage apparatus or devices. The term “machine-readable medium”excludes signals per se.

“COMPONENT” in this context refers to a device, a physical entity, orlogic having boundaries defined by function or subroutine calls, branchpoints, APIs, or other technologies that provide for the partitioning ormodularization of particular processing or control functions. Componentsmay be combined via their interfaces with other components to carry outa machine process. A component may be a packaged functional hardwareunit designed for use with other components and a part of a program thatusually performs a particular function of related functions. Componentsmay constitute either software components (e.g., code embodied on amachine-readable medium) or hardware components. A “hardware component”is a tangible unit capable of performing certain operations and may beconfigured or arranged in a certain physical manner. In various exampleembodiments, one or more computer systems (e.g., a standalone computersystem, a client computer system, or a server computer system) or one ormore hardware components of a computer system (e.g., a processor or agroup of processors) may be configured by software (e.g., an applicationor application portion) as a hardware component that operates to performcertain operations as described herein. A hardware component may also beimplemented mechanically, electronically, or any suitable combinationthereof. For example, a hardware component may include dedicatedcircuitry or logic that is permanently configured to perform certainoperations. A hardware component may be a special-purpose processor,such as a field-programmable gate array (FPGA) or anapplication-specific integrated circuit (ASIC). A hardware component mayalso include programmable logic or circuitry that is temporarilyconfigured by software to perform certain operations. For example, ahardware component may include software executed by a general-purposeprocessor or other programmable processor. Once configured by suchsoftware, hardware components become specific machines (or specificcomponents of a machine) uniquely tailored to perform the configuredfunctions and are no longer general-purpose processors. It will beappreciated that the decision to implement a hardware componentmechanically, in dedicated and permanently configured circuitry, or intemporarily configured circuitry (e.g., configured by software) may bedriven by cost and time considerations. Accordingly, the phrase“hardware component” (or “hardware-implemented component”) should beunderstood to encompass a tangible entity, be that an entity that isphysically constructed, permanently configured (e.g., hardwired), ortemporarily configured (e.g., programmed) to operate in a certain manneror to perform certain operations described herein. Consideringembodiments in which hardware components are temporarily configured(e.g., programmed), each of the hardware components need not beconfigured or instantiated at any one instance in time. For example,where a hardware component comprises a general-purpose processorconfigured by software to become a special-purpose processor, thegeneral-purpose processor may be configured as respectively differentspecial-purpose processors (e.g., comprising different hardwarecomponents) at different times. Software accordingly configures aparticular processor or processors, for example, to constitute aparticular hardware component at one instance of time and to constitutea different hardware component at a different instance of time. Hardwarecomponents can provide information to, and receive information from,other hardware components. Accordingly, the described hardwarecomponents may be regarded as being communicatively coupled. Wheremultiple hardware components exist contemporaneously, communications maybe achieved through signal transmission (e.g., over appropriate circuitsand buses) between or among two or more of the hardware components. Inembodiments in which multiple hardware components are configured orinstantiated at different times, communications between or among suchhardware components may be achieved, for example, through the storageand retrieval of information in memory structures to which the multiplehardware components have access. For example, one hardware component mayperform an operation and store the output of that operation in a memorydevice to which it is communicatively coupled. A further hardwarecomponent may then, at a later time, access the memory device toretrieve and process the stored output. Hardware components may alsoinitiate communications with input or output devices, and can operate ona resource (e.g., a collection of information). The various operationsof example methods described herein may be performed, at leastpartially, by one or more processors that are temporarily configured(e.g., by software) or permanently configured to perform the relevantoperations. Whether temporarily or permanently configured, suchprocessors may constitute processor-implemented components that operateto perform one or more operations or functions described herein. As usedherein, “processor-implemented component” refers to a hardware componentimplemented using one or more processors. Similarly, the methodsdescribed herein may be at least partially processor-implemented, with aparticular processor or processors being an example of hardware. Forexample, at least some of the operations of a method may be performed byone or more processors or processor-implemented components. Moreover,the one or more processors may also operate to support performance ofthe relevant operations in a “cloud computing” environment or as a“software as a service” (SaaS). For example, at least some of theoperations may be performed by a group of computers (as examples ofmachines including processors), with these operations being accessiblevia a network (e.g., the Internet) and via one or more appropriateinterfaces (e.g., an API). The performance of certain of the operationsmay be distributed among the processors, not only residing within asingle machine, but deployed across a number of machines. In someexample embodiments, the processors or processor-implemented componentsmay be located in a single geographic location (e.g., within a homeenvironment, an office environment, or a server farm). In other exampleembodiments, the processors or processor-implemented components may bedistributed across a number of geographic locations.

“PROCESSOR” in this context refers to any circuit or virtual circuit (aphysical circuit emulated by logic executing on an actual processor)that manipulates data values according to control signals (e.g.,“commands”, “op codes”, “machine code”, etc.) and which producescorresponding output signals that are applied to operate a machine. Aprocessor may, for example, be a Central Processing Unit (CPU), aReduced Instruction Set Computing (RISC) processor, a ComplexInstruction Set Computing (CISC) processor, a Graphics Processing Unit(GPU), a Digital Signal Processor (DSP), an ASIC, a Radio-FrequencyIntegrated Circuit (RFIC), or any combination thereof. A processor mayfurther be a multi-core processor having two or more independentprocessors (sometimes referred to as “cores”) that may executeinstructions contemporaneously.

“TIMESTAMP” in this context refers to a sequence of characters orencoded information identifying when a certain event occurred, forexample giving date and time of day, sometimes accurate to a smallfraction of a second.

A portion of the disclosure of this patent document contains materialthat is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent files or records, but otherwise reserves all copyrightrights whatsoever. The following notice applies to the software and dataas described below and in the drawings that form a part of thisdocument: Copyright 2017, SNAP INC., All Rights Reserved.

What is claimed is:
 1. A method comprising: storing, on a device, aconvolutional neural network trained to detect different gestures ofdifferent objects, the convolutional neural network trained on aplurality of images generated from three-dimensional models of thedifferent objects, each of the plurality of images depicting athree-dimensional model of one of the different objects in a combinationof the different gestures, textures, and three-dimensional environments;identifying an image depicting a physical object; and classifying, usingthe convolutional neural network operating on one or more processors ofthe device, the physical object as being in a gesture from the differentgestures of the different objects by applying the convolutional neuralnetwork to the image depicting the physical object.
 2. The method ofclaim 1, wherein one or more of the plurality of images depicts athree-dimensional model of one of the different objects with a skintexture in a different gesture.
 3. The method of claim 1, furthercomprising: identifying additional content associated with the gesture;and storing the additional content on the device.
 4. The method of claim3, wherein the additional content comprises user interface contentassociated with the gesture of the physical object in the image.
 5. Themethod of claim 1, wherein the plurality of images are rendered from aplurality of virtual cameras that view the three-dimensional model ofthe different objects from different perspectives.
 6. The method ofclaim 1, wherein the textures include a skin shade texture.
 7. Themethod of claim 6, wherein the plurality of images include another setof images depicting the three-dimensional model of the different objectswith another skin texture as arranged in the different gestures, theskin texture being a different skin texture than the another skintexture.
 8. The method of claim 1, wherein the convolutional neuralnetwork is trained on a network platform.
 9. The method of claim 8,further comprising: receiving, from the network platform, theconvolutional neural network.
 10. The method of claim 1, wherein thedifferent objects comprises at least one of: a hand, a face, a humanbody, or an animal body.
 11. A system comprising: one or more processorsof a machine; a memory storing instructions that, when executed by theone or more processors, cause the machine to perform operationscomprising: storing, on a device, a convolutional neural network trainedto detect different gestures of different objects, the convolutionalneural network trained on a plurality of images generated fromthree-dimensional models of the different objects, each of the pluralityof images depicting a three-dimensional model of one of the differentobjects in a combination of the different gestures, textures, andthree-dimensional environments; identifying an image depicting aphysical object; and classifying, using the convolutional neural networkoperating on one or more processors of the device, the physical objectas being in a gesture from the different gestures of the differentobjects by applying the convolutional neural network to the imagedepicting the physical object.
 12. The system of claim 11, wherein oneor more of the plurality of images depicts a three-dimensional model ofone of the different objects with a skin texture in a different gesture.13. The system of claim 11, wherein the operations further comprise:identifying additional content associated with the gesture; and storingthe additional content on the device.
 14. The system of claim 13,wherein the additional content comprises user interface contentassociated with the gesture of the physical object in the image.
 15. Thesystem of claim 11, wherein the plurality of images are rendered from aplurality of virtual cameras that view the three-dimensional model ofthe different objects from different perspectives.
 16. The system ofclaim 11, wherein the textures include a skin shade texture.
 17. Thesystem of claim 16, wherein the plurality of images include another setof images depicting the three-dimensional model of the different objectswith another skin texture as arranged in the different gestures, theskin texture being a different skin texture than the another skintexture.
 18. The system of claim 11, wherein the convolutional neuralnetwork is trained on a network platform.
 19. The system of claim 18,wherein the operations further comprise: receiving, from the networkplatform, the convolutional neural network.
 20. A non-transitorymachine-readable storage device embodying instructions that, whenexecuted by a machine, cause the machine to perform operationscomprising: storing, on a device, a convolutional neural network trainedto detect different gestures of different objects, the convolutionalneural network trained on a plurality of images generated fromthree-dimensional models of the different objects, each of the pluralityof images depicting a three-dimensional model of one of the differentobjects in a combination of the different gestures, textures, andthree-dimensional environments; identifying an image depicting aphysical object; and classifying, using the convolutional neural networkoperating on one or more processors of the machine, the physical objectas being in a gesture from the different gestures of the differentobjects by applying the convolutional neural network to the imagedepicting the physical object.